Attractor Reconstruction with Principal Components Analysis: Application to Work Flows in Hierarchical Organizations Export

@article{citeulike:2817801, abstract = {Explored several procedural questions in the use of the principal components technique for filtering time series data and reconstructing attractors in two or more dimensions. Data originated from an earlier study on work flows in experimental hierarchical organizations. The reconstructed time series and attractors were evaluated alongside a nonlinear regression modeling approach for assessing the dynamical properties of data. The results showed that, if the nonlinear function representing the time series is initially poorly fitted, principal components filtering will probably help to identify a nonlinear structure insofar as it has been fed enough data to capture the complexity of the time series of events. The relative merits of the eigenvalue 1.00 criterion and the scree test for determining the correct number of components were also addressed. Oblique rotation procedures are recommended.}, author = {Guastello, Stephen and Bock, Benjamin}, citeulike-article-id = {2817801}, citeulike-linkout-0 = {http://dx.doi.org/10.1023/A:1026471501949}, day = {1}, doi = {10.1023/A:1026471501949}, journal = {Nonlinear Dynamics, Psychology, and Life Sciences}, keywords = {analysis, application, attractor, components, flows, hierarchical, in, organizations, principal, reconstruction, to, with, work}, month = {April}, number = {2}, pages = {175--191}, posted-at = {2008-05-22 00:31:03}, priority = {2}, title = {Attractor Reconstruction with Principal Components Analysis: Application to Work Flows in Hierarchical Organizations}, url = {http://dx.doi.org/10.1023/A:1026471501949}, volume = {5}, year = {2001} }

TY - JOUR ID - citeulike:2817801 L3 - citeulike-article-id:2817801 N2 - Explored several procedural questions in the use of the principal components technique for filtering time series data and reconstructing attractors in two or more dimensions. Data originated from an earlier study on work flows in experimental hierarchical organizations. The reconstructed time series and attractors were evaluated alongside a nonlinear regression modeling approach for assessing the dynamical properties of data. The results showed that, if the nonlinear function representing the time series is initially poorly fitted, principal components filtering will probably help to identify a nonlinear structure insofar as it has been fed enough data to capture the complexity of the time series of events. The relative merits of the eigenvalue 1.00 criterion and the scree test for determining the correct number of components were also addressed. Oblique rotation procedures are recommended. IS - 2 JF - Nonlinear Dynamics, Psychology, and Life Sciences EP - 191 TI - Attractor Reconstruction with Principal Components Analysis: Application to Work Flows in Hierarchical Organizations VL - 5 SP - 175 KW - analysis KW - application KW - attractor KW - components KW - flows KW - hierarchical KW - in KW - organizations KW - principal KW - reconstruction KW - to KW - with KW - work AU - Guastello, Stephen AU - Bock, Benjamin PY - 2001/04/01/ UR - http://dx.doi.org/10.1023/A:1026471501949 ER -